Abstract
Appropriate modelling of soil behaviour is essential when dealing with issue related to soil mechanics and foundation engineering. In this paper, a new data-driven methodology is used for simulating and predicting shear behaviour of soils in both drained and undrained conditions. The proposed evolutionary based technique is capable of generating transparent and structured representation of the triaxial test data provided. Excellent agreements between the experimental data and the modelling results are observed in both cases. In addition, “feed-forward” algorithms are proposed separately for drained and undrained conditions, in order to simulate stress-strain paths using well trained machine learning-based constitutive models. It is shown that a machine learning-based constitutive model which has been trained to capture soil behaviour using a limited number of triaxial test results, can also be employed as a stand-alone tool to generate additional virtual triaxial data with a very high accuracy.
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Acknowledgement
The financial support by the National Science Foundation of China (Contract 51850410511) is gratefully acknowledged. The second author also expresses appreciation to the financial support from Chinese Scholarship Council and The University of Warwick.
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Rezania, M., Ma, G. (2020). Stress-Strain Modelling of Soils in Drained and Undrained Conditions Using a Multi-model Intelligent Approach. In: Correia, A., Tinoco, J., Cortez, P., Lamas, L. (eds) Information Technology in Geo-Engineering. ICITG 2019. Springer Series in Geomechanics and Geoengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-32029-4_36
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DOI: https://doi.org/10.1007/978-3-030-32029-4_36
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